






"""simple docstring"""



from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin





@dataclass
class    _UpperCAmelCase			(					__snake_case				):
					SCREAMING_SNAKE_CASE_						:							torch.FloatTensor





class    _UpperCAmelCase			(					__snake_case  ,		__snake_case				):




					@register_to_config
					def __init__(							self	:		Any       ,		A	:		int = 32       ,		A	:		int = 64       ,		A	:		int = 20       ,		A	:		int = 7_68       ,		A	:		int=77       ,		A	:		Any=4       ,		A	:		float = 0.0       ,		A	:		str = "silu"       ,		A	:		Optional[str] = None       ,		A	:		Optional[str] = None       ,		A	:		Optional[str] = "linear"       ,		A	:		Optional[str] = "prd"       ,		A	:		Optional[int] = None       ,		A	:		Optional[int] = None       ,		A	:		Optional[int] = None       ,		)    ->    List[Any]:
							super().__init__()
							lowercase_					:					Tuple							=  num_attention_heads
							lowercase_					:					Any							=  attention_head_dim
							lowercase_					:					Tuple							=  num_attention_heads * attention_head_dim
							lowercase_					:					Union[str, Any]							=  additional_embeddings

							lowercase_					:					Any							=  time_embed_dim or inner_dim
							lowercase_					:					List[Any]							=  embedding_proj_dim or embedding_dim
							lowercase_					:					str							=  clip_embed_dim or embedding_dim

							lowercase_					:					int							=  Timesteps(__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE       ,		0  )
							lowercase_					:					Union[str, Any]							=  TimestepEmbedding(__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE       ,		out_dim=__SCREAMING_SNAKE_CASE       ,		act_fn=__SCREAMING_SNAKE_CASE  )

							lowercase_					:					Tuple							=  nn.Linear(__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE  )

							if embedding_proj_norm_type is None:
									lowercase_					:					List[Any]							=  None
							elif embedding_proj_norm_type == "layer":
									lowercase_					:					int							=  nn.LayerNorm(__SCREAMING_SNAKE_CASE  )
							else:
									raise ValueError(F'''unsupported embedding_proj_norm_type: {embedding_proj_norm_type}'''  )

							lowercase_					:					List[Any]							=  nn.Linear(__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE  )

							if encoder_hid_proj_type is None:
									lowercase_					:					Union[str, Any]							=  None
							elif encoder_hid_proj_type == "linear":
									lowercase_					:					Tuple							=  nn.Linear(__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE  )
							else:
									raise ValueError(F'''unsupported encoder_hid_proj_type: {encoder_hid_proj_type}'''  )

							lowercase_					:					int							=  nn.Parameter(torch.zeros(1       ,		num_embeddings + additional_embeddings       ,		__SCREAMING_SNAKE_CASE  )  )

							if added_emb_type == "prd":
									lowercase_					:					Union[str, Any]							=  nn.Parameter(torch.zeros(1       ,		1       ,		__SCREAMING_SNAKE_CASE  )  )
							elif added_emb_type is None:
									lowercase_					:					str							=  None
							else:
									raise ValueError(
									    F'''`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.'''  )

							lowercase_					:					Any							=  nn.ModuleList(
							    [
							        BasicTransformerBlock(
							            __SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE       ,		dropout=__SCREAMING_SNAKE_CASE       ,		activation_fn='''gelu'''       ,		attention_bias=__SCREAMING_SNAKE_CASE       ,		)
							        for d in range(__SCREAMING_SNAKE_CASE  )
							    ]  )

							if norm_in_type == "layer":
									lowercase_					:					Tuple							=  nn.LayerNorm(__SCREAMING_SNAKE_CASE  )
							elif norm_in_type is None:
									lowercase_					:					Optional[Any]							=  None
							else:
									raise ValueError(F'''Unsupported norm_in_type: {norm_in_type}.'''  )

							lowercase_					:					List[str]							=  nn.LayerNorm(__SCREAMING_SNAKE_CASE  )

							lowercase_					:					List[Any]							=  nn.Linear(__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE  )

							lowercase_					:					Optional[int]							=  torch.full(
							    [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]       ,		-1_00_00.0  )
							causal_attention_mask.triu_(1  )
							lowercase_					:					Any							=  causal_attention_mask[None, ...]
							self.register_buffer('''causal_attention_mask'''       ,		__SCREAMING_SNAKE_CASE       ,		persistent=__SCREAMING_SNAKE_CASE  )

							lowercase_					:					Tuple							=  nn.Parameter(torch.zeros(1       ,		__SCREAMING_SNAKE_CASE  )  )
							lowercase_					:					Optional[Any]							=  nn.Parameter(torch.zeros(1       ,		__SCREAMING_SNAKE_CASE  )  )




					@property
					# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
					def     A    (							self	:		List[str]  )    ->    Dict[str, AttentionProcessor]:
							lowercase_					:					List[Any]							=  {}

							def fn_recursive_add_processors(A	:		str       ,		A	:		torch.nn.Module       ,		A	:		Dict[str, AttentionProcessor]  ):
									if hasattr(__SCREAMING_SNAKE_CASE       ,		'''set_processor'''  ):
											lowercase_					:					Any							=  module.processor

									for sub_name, child in module.named_children():
											fn_recursive_add_processors(F'''{name}.{sub_name}'''       ,		__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE  )

									return processors

							for name, module in self.named_children():
									fn_recursive_add_processors(__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE  )

							return processors




					def     A    (							self	:		Optional[Any]       ,		A	:		Union[AttentionProcessor, Dict[str, AttentionProcessor]]  )    ->    Optional[int]:
							lowercase_					:					Union[str, Any]							=  len(self.attn_processors.keys()  )

							if isinstance(__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE  ) and len(__SCREAMING_SNAKE_CASE  ) != count:
									raise ValueError(
									    F'''A dict of processors was passed, but the number of processors {len(__SCREAMING_SNAKE_CASE  )} does not match the'''
									    F''' number of attention layers: {count}. Please make sure to pass {count} processor classes.'''  )

							def fn_recursive_attn_processor(A	:		str       ,		A	:		torch.nn.Module       ,		A	:		str  ):
									if hasattr(__SCREAMING_SNAKE_CASE       ,		'''set_processor'''  ):
											if not isinstance(__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE  ):
													module.set_processor(__SCREAMING_SNAKE_CASE  )
											else:
													module.set_processor(processor.pop(F'''{name}.processor'''  )  )

									for sub_name, child in module.named_children():
											fn_recursive_attn_processor(F'''{name}.{sub_name}'''       ,		__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE  )

							for name, module in self.named_children():
									fn_recursive_attn_processor(__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE  )




					def     A    (							self	:		Any  )    ->    int:
							self.set_attn_processor(AttnProcessor()  )




					def     A    (							self	:		str       ,		A	:		Dict       ,		A	:		Union[torch.Tensor, float, int]       ,		A	:		torch.FloatTensor       ,		A	:		Optional[torch.FloatTensor] = None       ,		A	:		Optional[torch.BoolTensor] = None       ,		A	:		bool = True       ,		)    ->    List[Any]:
							lowercase_					:					Union[str, Any]							=  hidden_states.shape[0]

							lowercase_					:					Any							=  timestep
							if not torch.is_tensor(__SCREAMING_SNAKE_CASE  ):
									lowercase_					:					List[str]							=  torch.tensor([timesteps]       ,		dtype=torch.long       ,		device=hidden_states.device  )
							elif torch.is_tensor(__SCREAMING_SNAKE_CASE  ) and len(timesteps.shape  ) == 0:
									lowercase_					:					List[str]							=  timesteps[None].to(hidden_states.device  )

							# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
							lowercase_					:					Dict							=  timesteps * torch.ones(__SCREAMING_SNAKE_CASE       ,		dtype=timesteps.dtype       ,		device=timesteps.device  )

							lowercase_					:					List[Any]							=  self.time_proj(__SCREAMING_SNAKE_CASE  )

							# timesteps does not contain any weights and will always return f32 tensors
							# but time_embedding might be fp16, so we need to cast here.
							lowercase_					:					Union[str, Any]							=  timesteps_projected.to(dtype=self.dtype  )
							lowercase_					:					Tuple							=  self.time_embedding(__SCREAMING_SNAKE_CASE  )

							if self.embedding_proj_norm is not None:
									lowercase_					:					int							=  self.embedding_proj_norm(__SCREAMING_SNAKE_CASE  )

							lowercase_					:					Union[str, Any]							=  self.embedding_proj(__SCREAMING_SNAKE_CASE  )
							if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
									lowercase_					:					List[Any]							=  self.encoder_hidden_states_proj(__SCREAMING_SNAKE_CASE  )
							elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
									raise ValueError('''`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set'''  )

							lowercase_					:					Optional[Any]							=  self.proj_in(__SCREAMING_SNAKE_CASE  )

							lowercase_					:					Optional[int]							=  self.positional_embedding.to(hidden_states.dtype  )

							lowercase_					:					str							=  []
							lowercase_					:					Union[str, Any]							=  0

							if encoder_hidden_states is not None:
									additional_embeds.append(__SCREAMING_SNAKE_CASE  )
									additional_embeddings_len += encoder_hidden_states.shape[1]

							if len(proj_embeddings.shape  ) == 2:
									lowercase_					:					str							=  proj_embeddings[:, None, :]

							if len(hidden_states.shape  ) == 2:
									lowercase_					:					Tuple							=  hidden_states[:, None, :]

							lowercase_					:					int							=  additional_embeds + [
							    proj_embeddings,
							    time_embeddings[:, None, :],
							    hidden_states,
							]

							if self.prd_embedding is not None:
									lowercase_					:					Any							=  self.prd_embedding.to(hidden_states.dtype  ).expand(__SCREAMING_SNAKE_CASE       ,		-1       ,		-1  )
									additional_embeds.append(__SCREAMING_SNAKE_CASE  )

							lowercase_					:					Any							=  torch.cat(
							    __SCREAMING_SNAKE_CASE       ,		dim=1       ,		)

							# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
							lowercase_					:					Union[str, Any]							=  additional_embeddings_len + proj_embeddings.shape[1] + 1
							if positional_embeddings.shape[1] < hidden_states.shape[1]:
									lowercase_					:					str							=  F.pad(
									    __SCREAMING_SNAKE_CASE       ,		(
									        0,
									        0,
									        additional_embeddings_len,
									        self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
									    )       ,		value=0.0       ,		)

							lowercase_					:					Union[str, Any]							=  hidden_states + positional_embeddings

							if attention_mask is not None:
									lowercase_					:					int							=  (1 - attention_mask.to(hidden_states.dtype  )) * -1_00_00.0
									lowercase_					:					Dict							=  F.pad(__SCREAMING_SNAKE_CASE       ,		(0, self.additional_embeddings)       ,		value=0.0  )
									lowercase_					:					int							=  (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype  )
									lowercase_					:					Dict							=  attention_mask.repeat_interleave(self.config.num_attention_heads       ,		dim=0  )

							if self.norm_in is not None:
									lowercase_					:					Dict							=  self.norm_in(__SCREAMING_SNAKE_CASE  )

							for block in self.transformer_blocks:
									lowercase_					:					Union[str, Any]							=  block(__SCREAMING_SNAKE_CASE       ,		attention_mask=__SCREAMING_SNAKE_CASE  )

							lowercase_					:					Tuple							=  self.norm_out(__SCREAMING_SNAKE_CASE  )

							if self.prd_embedding is not None:
									lowercase_					:					Optional[Any]							=  hidden_states[:, -1]
							else:
									lowercase_					:					str							=  hidden_states[:, additional_embeddings_len:]

							lowercase_					:					Optional[Any]							=  self.proj_to_clip_embeddings(__SCREAMING_SNAKE_CASE  )

							if not return_dict:
									return (predicted_image_embedding,)

							return PriorTransformerOutput(predicted_image_embedding=__SCREAMING_SNAKE_CASE  )






					def     A    (							self	:		Optional[Any]       ,		A	:		Optional[int]  )    ->    Optional[int]:
							lowercase_					:					Tuple							=  (prior_latents * self.clip_std) + self.clip_mean
							return prior_latents











"""simple docstring"""





import functools
from typing import Any







def 	lowerCamelCase__			(				_lowerCamelCase			:						str						,							_lowerCamelCase			:						list[str]					) ->							bool:

  # Validation
  if not isinstance(_lowerCamelCase						,							_lowerCamelCase					) or len(_lowerCamelCase					) == 0:
    raise ValueError('the string should be not empty string'					)

  if not isinstance(_lowerCamelCase						,							_lowerCamelCase					) or not all(
      isinstance(_lowerCamelCase						,							_lowerCamelCase					) and len(_lowerCamelCase					) > 0 for item in words					):
    raise ValueError('the words should be a list of non-empty strings'					)

  # Build trie
  lowerCamelCase_       	=		{}
  lowerCamelCase_       	=		'WORD_KEEPER'

  for word in words:
    lowerCamelCase_       	=		trie
    for c in word:
      if c not in trie_node:
        lowerCamelCase_       	=		{}

      lowerCamelCase_       	=		trie_node[c]

    lowerCamelCase_       	=		True

  lowerCamelCase_       	=		len(_lowerCamelCase					)

  # Dynamic programming method
  @functools.cache
  def is_breakable(_lowerCamelCase			:						int					) -> bool:
    if index == len_string:
      return True

    lowerCamelCase_       	=		trie
    for i in range(_lowerCamelCase						,							_lowerCamelCase					):
      lowerCamelCase_       	=		trie_node.get(string[i]						,							_lowerCamelCase					)

      if trie_node is None:
        return False

      if trie_node.get(_lowerCamelCase						,							_lowerCamelCase					) and is_breakable(i + 1					):
        return True

    return False

  return is_breakable(0					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()




